Submitted for the project viva voce examination held on _______________

INTERNAL EXAMINER EXTERNAL EXAMINER

2 ACKNOWLEDGEMENTS

I would like to take this opportunity to thank each and every one who had helped methroughout the entire project in making great success.On the forefront, I would like to extend my gratitude to Prof. M. Sivanandham,Secretary and Visiting Professor of Biotechnology, Sri Venkateswara College ofEngineering for providing the support.I would like to thank our Principal of Sri Venkateswara College of Engineering,Prof. S. Ganesh Vaidyanathan for giving me the opportunity to do my project in thecollege.I sincerely thank Prof. E. Nakkeeran, Head of the Department of Biotechnology forindividually looking through my project.It would be incomplete if I do not thank my project guide, Ms. N. Kanagam,Assistant Professor, Department of Biotechnology, for monitoring the projectcompletely and for guiding me the right way without which this project would nothave been possible.I would like to thank Prof. Nalinkanth. V. Ghone, Professor, Department ofBiotechnology, for all his advice and support.I would also like to thank Mr. J. Hariharan, Assistant Professor, Department ofBiotechnology, for his invaluable help with genetic engineering work.I would like to extend my sincere gratitude to all the other faculty members and labassistants for their valuable ideas and help. Finally, I would like to thank my parentsand friends for their encouragement and cooperation. Saishreyas Gourishankar Iyer

3 ABSTRACT

Antimicrobial peptides have existed as a part of the innate immune response of all

classes of life for millennia. Yet even today it remains one of the new and under-

researched areas of biology. Though detailed study of a few well known

antimicrobial peptides can give us valuable insights into its structure, function,

mechanism of action and targets, a more broad minded study is also required to

gain a deeper understanding into the science behind these remarkable molecules.

The present study has focused on the designing of antimicrobial decapeptides

1.1 : ANTIMICROBIAL PEPTIDES

proteins with broad spectrum antimicrobial activity against bacteria, viruses, andfungi. They are a part of the innate immune response found among all classes oflife. Antimicrobial peptides are finding a lot of applications in a variety of fields.Synthetic peptides could even replace conventional antibiotics because of theirspecificity, speed and because bacteria cannot easily develop resistance towardsthem. They have been demonstrated to kill Gram negative and Grampositive bacteria, enveloped viruses, fungi and transformed or cancerous cells(Reddy et al., 2004). Unlike the majority of conventional antibiotics it appears asthough antimicrobial peptides may also have the ability to enhance immunity byfunctioning as immunomodulators. Antimicrobial peptides are generally between12 and 50 amino acids. These peptides include two or more positively chargedresidues provided by arginine, lysine or, in acidic environments, histidine, and alarge proportion (generally >50%) of hydrophobic residues. The secondarystructures of these molecules follow 4 themes, including i) α helical, ii) β strandeddue to the presence of 2 or more disulfide bonds, iii) β-hairpin or loop due to thepresence of a single disulfide bond and/or cyclization of the peptide chain, and iv)extended.

16 Figure 1 : Various structures of antimicrobial peptides

This image shows the 4 main classes of antimicrobial peptides with examples.(source :en.wikipedia.org/wiki/Antimicrobial_peptides#/media/File:Various_AMPs.png)

Figure 2 : Mechanism of action of antimicrobial peptides

There are many proposed modes, the main ones being formation of a pore in the membrane andpenetration(source:en.wikipedia.org/wiki/Antimicrobial_peptides#/media/File:Modes_of_action.png)

171.2 : PREVIOUS WORK DONE

A major problem faced by the medical world is the problem of antimicrobial

resistance. Many scientists are turning towards synthetic peptides that are morespecific and potent than their natural counterparts. One such class of peptides is theshort Cationic Antimicrobial Peptides (scAMPs) (Wenzel et al., 2014). A study byWenzel et al., (2014) showed that scAMPs made of arginine and tryptophan hadpotent antimicrobial activity. They showed that these peptides killed many Grampositive and Gram negative organisms by delocalization of peripheral membraneproteins. The positive charge of arginine and the lipophilic nature of tryptophanwere shown to be responsible for the antimicrobial activity.

The SVCE CHENNAI team of iGEM 2016 did a project that could eradicate theproblem of milk spoilage. They designed a sachet system for delivering AMPsinto the milk for killing the microorganisms present in milk.

Figure 3 : The sachet system designed by iGEM 2016

Two small cationic antimicrobial peptides (cAMPs) were designed; both have

These cAMPs kill the bacterial cells through different mechanisms that dependmainly on the unique properties of arginine and tryptophan. Guanidinium group ofarginine forms bidentate hydrogen bonds with negatively charged phosphate,sulfate and carboxylate groups on the cell surface. The aromatic side chain intryptophan, helps it establish π- anion interactions with the sulfate groups of GAGsin the bacterial cell wall, allowing the peptide to insert itself into the cell wall.Two separate constructs were designed to make the process of AMP productiontemperature controlled – one using an RNA thermometer and the other using a λpRpromoter.1.3 : THE PRESENT STUDYEvery project done on AMPs takes us one step closer to understanding the sciencebehind these remarkable proteins.The present study focuses on the following1. To theoretically design and validate antimicrobial peptides using bioinformaticstools.2. To come up with a few best AMPs from among all of the ones designed.3. To model each of the AMPs and to do cluster calculation using Liquid POPC assolvent.4. To test one of the designed AMPs in the laboratory.

19The present study studies the effect of alternating amino acid residues on theantimicrobial index of the peptide in which they are present. It explores thepossibility of very small peptides being potential AMP candidates. It shows us thattwo peptides with the same amino acid composition need not have the sameproperties.

20 CHAPTER 2

REVIEW OF LITERATURE

A lot of research has been done in the field of antimicrobial peptides, but still, nota lot is known about their mechanism of action, their target and their potency. A lotof web servers are available that offer tools for predicting antimicrobial regions inthe query peptide. But every website bases its prediction on a different set of facts.The present study focuses on designing antimicrobial peptide candidates andtesting at least one of the designed peptides in the lab for efficiency. The designingprocess has been done using 3 of the best antimicrobial peptide prediction toolsavailable online, each one basing their prediction on a different set of valid facts.

This has been done to get an even more accurate prediction of the potentialantimicrobial peptide candidates.

The first prediction tool used is AMPA Antimicrobial Sequence Scanning System.The authors have used the antimicrobial peptide bactenecin 2A for their study inwhich they have found its IC50 values for every one of its amino acidreplacements. They have come up with an antimicrobial index for every aminoacid that gives a fair assessment of the tendency of any amino acid to be foundwithin the AMP sequence.

Torrent et al., (2012), have concluded that AMPA-derived AI values can be used toautomatically classify proteins or domains as either antimicrobial or non-

21antimicrobial. They have said “The algorithm has been extensively validated insilico and found to correctly identify 80–90% of the antimicrobial proteins andproperly predict their antimicrobial domain.”

The second level of prediction was done using APD3 server which predictsantimicrobial peptides based on whether the peptides have hydrophobic residuesand what their net surface charge is. It also compares the peptide with all the otherantimicrobial peptides in its database.

Wang et al., (2015), have improved the peptide prediction interface in APD3. Itnow predicts the possibility of a sequence to be AMPs based on the entireparameter space defined by all the natural peptides registered in the database.The third level of prediction was done using CAMP - Collection of Anti MicrobialPeptides server’s prediction tool which uses Artificial Neural Network,Discriminant Analysis, Support Vector Machine and Random Forest Classifier tocompare the query peptide to all the peptides in their database.Waghu et al., (2016), have based their tool on the fact that antimicrobial peptides(AMPs) are known to have family-specific sequence composition, which can bemined for discovery and design of AMPs. CAMP is a database of sequences,structures and family-specific signatures of prokaryotic and eukaryotic AMPs.CAMPR3 presently holds 10247 sequences, 757 structures and 114 family-specificsignatures of AMPs. Users can use the sequence optimization algorithm forrational design of AMPs. The database integrated with tools for AMP sequenceand structure analysis will be a valuable resource for family-based studies onAMPs.

22Tyagi et al., (2013), that usually Cys, Gly, Ile, Lys, and Trp are dominated atvarious positions in anticancer peptides. They developed support vector machine(SVM) based models using various features of peptides like amino acidcomposition, dipeptide composition and binary profile pattern In addition, modelsdiscriminating ACPs from AMPs have also been developed by them. Theprediction tool gives the output as an SVM score. The higher the score, the better.Wenzel et al., (2014), have tested the antimicrobial effects of a hexapeptide havingalternating arginine and tryptophan residues on a range of microorganisms andhave come to the conclusion that it is a potential antimicrobial candidate. Theirstudy shows that the positive charge of arginine and the hydrophobic nature oftryptophan contribute significantly to the antimicrobial property of the protein theyare part of.

Petersen et al., (2011), have used three types of scores, C, S and Y scores to predictsignal peptide regions within proteins.

The C-score is trained to be high at the position immediately after the cleavagesite.

S-score (signal peptide score) distinguishes positions within signal peptides frompositions in the mature part of the proteins and from proteins without signalpeptides.

Y-score (combined cleavage site score)is combination (geometric average) of the

C-score and the slope of the S-score, resulting in a better cleavage site predictionthan the raw C-score alone. The Y-score distinguishes between C-score peaks by

23choosing the one where the slope of the S-score is steep.

24 CHAPTER 3

AIM AND OBJECTIVE

Aim : To design, construct and express an antimicrobial peptide in Escherichia

coli.

Objective :

1. To design several antimicrobial peptides and to compare their physical, chemical

and antimicrobial properties.

2. To select a few peptides with the best antimicrobial properties, to model themand to perform cluster calculation for these peptides using Liquid POPC as solvent.

3. To express an antimicrobial peptide in E.coli and then test it against a range of

microorganisms.

25 CHAPTER 4

MATERIALS AND METHODS

4.1 : IN SILICO STUDIES

4.1.1 : The designing of the antimicrobial peptides.

The antimicrobial indices given in the AMPA Antimicrobial Sequence Scanning

System (Torrent et al., 2012) was used to design only a limited number of peptidesbased on the antimicrobial indices of the individual amino acids. The AMPAalgorithm uses an antimicrobial propensity scale to generate an antimicrobialprofile by means of a sliding window system. The propensity scale has beenderived using high-throughput screening results from the AMP bactenecin 2A, a12-residue peptide for which antimicrobial IC50 values for all amino acidreplacements at each position have been determined. From the IC50 for eachsubstitution, an antimicrobial index for individual residues can be calculated (Table1) that provides a fair assessment of the tendency of such amino acid to be foundwithin an AMP sequence.

26 Table 1 : Antimicrobial index of amino acid residues

The present study is based on the top 5 amino acids in this table. (source : AMPA website)

Residue Arg Lys Cys Trp Tyr Ile Val

PV 0.106 0.111 0.165 0.172 0.185 0.198 0.200

Residue His Asn Thr Phe Leu Gln Gly

PV 0.202 0.240 0.242 0.246 0.246 0.248 0.265

Residue Met Ser Ala Pro Glu Asp

PV 0.265 0.281 0.307 0.327 0.449 0.479

As low IC50 values correspond to high activity, amino acids with a lowantimicrobial index are the most favored to be part of an AMP. Each peptideconsisted of two alternating amino acid residues to give a total of ten residues foreach peptide. The amino acids with the top five antimicrobial indices according tothe AMPA website were selected for this purpose. These are –

Arginine – 0.106

Lysine – 0.111

Cysteine – 0.165

Tryptophan – 0.172

27Tyrosine – 0.185

The lower the antimicrobial index of an amino acid, the better the candidate fordesigning an antimicrobial peptide (AMP). (Torrent et al., 2009) Decapeptidesconsisting of the above listed amino acids, each, combined with all the other 20amino acids were written down along with the reverse peptide for each peptidedesigned. The total came to 175 decapeptides.

4.1.2 : Antimicrobial index for each decapeptide

All the decapeptides were run in the AMPA website tool to determine the meanantimicrobial index of each peptide. The window was set as 5 and the threshold as0.225. The peptides were ranked according to the results, with the peptides withthe lowest mean antimicrobial indices being considered the best. It was observedthat the reverse decapeptide for any decapeptide always had the same meanantimicrobial index, even though, maximum and the minimum antimicrobial indexfor every amino acid within the peptide showed variation between the twopeptides. Only those decapeptide with an index below 0.24 were selected for thenext step. Only 80 decapeptides made it through this process.

AMPA website predicts antimicrobial property based only on the antimicrobial

index. But to create really sound antimicrobial peptides, using this sorting processalone wasn’t enough. The antimicrobial property of any AMP depends not only onwhether the constituent amino acids have low antimicrobial index but also on thephysical and the chemical properties of these amino acids. One such property thatappears to be crucial to antimicrobial activity is the hydrophobicity of the amino

28acids.

It has been observed that hydrophobic amino acids contribute significantly to theantimicrobial activity of any AMP they are part of. (Wang and Wang., 2004;Wenzel et al., 2014).

4.1.3 : Presence of hydrophobic amino acids

from six kingdoms (294 bacteriocins and peptide antibiotics from bacteria, 4 fromarchaea, 8 from protists, 13 from fungi, 341 from plants, and 2116 from animals).It also has an “Antimicrobial Peptide Predictor and Calculator” tool.

It gives the mean antimicrobial index of the query peptide. (source: APD3 website)

29This tool was used to find the Wimley White Whole Residue Hydrophobicity andthe Bowman Index for all the peptides. In the APD3 Antimicrobial PeptideDatabase website, the Peptide design -> Improve Peptide options were selected andeach of the 80 peptides were entered as query. Based on the above observation, allthe decapeptides that did not contain any hydrophobic residues were eliminated.

4.1.4 : CAMP R3 (Collection of Anti-Microbial Peptides) prediction

The CAMP website AMP prediction tool was used. This tool has 4 differentoutputs.

(source : CAMP website)

31All 4 outputs were considered and only the peptides which scored a maximumpositive result in at least 3 output formats were selected. (Waghu et al., 2015)

4.1.5 : SignalP 4.1 server prediction

One of the most important properties of antimicrobial peptides that are expressedby any host organism is their ability to exit the host cell and interact with the targetcell. While their interaction with the target cell is determined mostly by surfacecharge (Wenzel et al., 2014), their ability to leave the host cell depends onpresence of specific signal peptide sequences within the peptide. (Petersen et al.,2011).Whether the designed peptides could be signal peptides or could contain asignal peptide or not was predicted using the SignalP 4.1 Server.

Figure 7 : The SignalP 4.1 Server.

(source: SignalP 4.1 server)

Figure 8 : The AntiCP server for predicting anticancer peptides

(source : the AntiCP website)

4.1.7 : Secondary structure prediction

To predict the secondary structure of the peptides, the PEP-FOLD 3 de novopeptide structure prediction site was used (Lamiable et al., 2016) The predictedstructure was then fed as pdb files into Ascalaph Designer. The “short optimizationto remove atomic clashes” option was used to do Energy Minimization.

The Energy Minimized structures were then fed into Vega ZZ software to visualizethe 3D structure of the peptides in tube form. Vega ZZ is the evolution of the wellknown VEGA OpenGL package and includes several new features andenhancements. VEGA was originally developed to create a bridge between most ofthe molecular software packages only, but in the years, enhancing its features, it'sevolved to a complete molecular modeling suite. This software is FREE for non-profit academic uses.

Then cluster calculation was performed on each of the peptides with the solventset as POPC-LIQUID, thickness = 20Å, overlap = 0.8Å, type = layer and clusterposition as geometry center.POPC is a chemical compound. It is a diacylglycerol and phospholipid. The fullname is 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine. It is an important

34phospholipid for biophysical experiments and has been used to study varioussubjects such as lipid rafts.

Figure 10 : The VEGA ZZ Molecular Modelling software

(source : Vega ZZ software)

4.1.8 : Selection of antimicrobial peptide for laboratory work

The AMP having arginine and tryptophan was selected for lab work. A comparisonwas made among random peptides all having arginine and tryptophan.

4.2 : LABORATORY WORK (Ausubel, F.M., 1999)

354.2.1 : RestrictionPrinciple :A restriction digest is a procedure used in molecular biology to prepare DNA foranalysis or other processing. Restriction digest is most commonly used as part ofthe process of the molecular cloning of DNA fragment into a vector. The vectorcontains a multiple cloning site where many restriction site may be found, and aforeign piece of DNA may be inserted into the vector by first cutting the restrictionsites in the vector as well the DNA fragment, followed by ligation of the DNAfragment into the vector.Materials required :1. Ice container

2. Microcentrifuge tubes

3. Purified DNA (>16 ng/µl)

4. Double distilled water (nuclease-free)

5. BSA (4 mg/ml)

6. Restriction enzymes and their respective buffers (10x)

Procedure :

1. About 2 µl of the DNA to be restricted was added to a microcentrifuge tube

placed on ice.

362. 2 µl of the restriction enzyme buffer was added.

3. 0.5 µl of BSA was added. (Addition of BSA enhances performance of the

restriction enzyme by providing additional protein to stabilize the enzyme andbalances side-effects arising as a result of enzyme interaction with solid surfaceslike the walls of the tube).

4. 0.5 µl of the restriction enzyme was added.

5. The reaction volume was added to 20 µl with distilled water (The 10x buffergets diluted to 1x).

6. The reaction was incubated at 37°C for 30 minutes.

7. To denature the enzymes present in the reaction mix, the mixture was kept at80°C for 20 minutes and the digested fragments were visualized by agarose gelelectrophoresis.

4.2.2 : Ligation

Principle :

Ligation is the joining of two nucleic acid fragments through the action of anenzyme. It is an essential laboratory procedure in the molecular cloning of DNAwhereby DNA fragments are joined together to create recombinant DNAmolecules, such as when a foreign DNA fragment is inserted into a plasmid. Theends of DNA fragments are joined together by the formation of phosphodiesterbonds between the 3'-hydroxyl of one DNA terminus with the 5'-phosphoryl of

37another. RNA may also be ligated similarly. A co-factor is generally involved inthe reaction, and this is usually ATP or NAD +. Ligation in the laboratory isnormally performed using T4 DNA Ligase enzyme.

3. The pellet was resuspended in 80 ml of cold CCMB80 buffer.

5. The contents was centrifuged at 3000xg for 10 minutes at 4°C.

7. 50 µl samples were aliquoted and stored at -80°C.

4.2.5 : Transformation :

Principle :

The competent cells take up the DNA provided externally. They then growselectively on the plates having the antibiotic towards which they are carrying theresistance in their newly acquired plasmid DNA.

5. Centrifugation was done for 10 minutes at 13000 rpm.

6. The supernatant was transferred to a fresh tube.

7. Around 500 µl of isopropanol was added.

8. Centrifugation was done at 8000 rpm for 10 minutes.

9. The supernatant was discarded and the pellet was washed twice using 70%ethanol.

10. The tubes were air dried and the pellet was dissolved in 20 µl of 0.5x TEbuffer. The tubes were stored at -20°C.

44 CHAPTER 5 RESULTS AND DISCUSSION

5.1 : IN SILICO STUDIES

5.1.1 : First tier elimination

The AMPA Antimicrobial Sequence Scanning System (Torrent et al., 2012) wasused to design only a limited number of peptides based on the antimicrobial indicesof the individual amino acids. The lower the antimicrobial index of an amino acid,the better the candidate for designing an antimicrobial peptide (AMP) (Torrent etal., 2012). Decapeptides consisting of the arginine, lysine, cysteine, tryptophan andtyrosine, each, combined with all the other 20 amino acids were written downalong with the reverse peptide for each peptide designed. The total came to 175decapeptides.

The window was set as 5 and the threshold as 0.225 and the mean antimicrobialindex was determined for each of the 175 peptides. The peptides were rankedaccording to the results, with the peptides with the lowest mean antimicrobialindices being considered the best. It was observed that the reverse decapeptide forany decapeptide always had the same mean antimicrobial index, even though,maximum and the minimum antimicrobial index for every amino acid within thepeptide showed variation between the two peptides. Only those decapeptide withan index below 0.24 were selected for the next step. Only 80 decapeptides made itthrough this process.

465.1.2 : Second tier elimination

In the APD3 Antimicrobial Peptide Database website, the Peptide design ->Improve Peptide options were selected and each of the 80 peptides were entered asquery. Based on the above observation, all the decapeptides that did not containany hydrophobic residues were eliminated. Only 49 decapeptides remained, inwhich 32 had a predicted net positive charge, a property viewed as favorable whiledesigning an AMP, while 17 had predicted no net charge. Nevertheless, thephysical and chemical properties for all 49 peptides were determined usingExPASy ProtParam website. The Instability Index varied from a highest score of295.4 for the peptide N – RWRWRWRWRW – C to a lowest score of -67.41 forthe peptides N – KIKIKIKIKI – C, N – IKIKIKIKIK – C, N – KLKLKLKLKL –C, N – LKLKLKLKLK – C, while for approximately 1/3 of the peptides, it was aconstant value of 9. The lower the Instability Index, the better. The Theoretical pIvaried from a highest value of 12.6 for about 1/3 of the peptides to a lowest valueof 5.49 for N – CYCYCYCYCY – C and N – YCYCYCYCYC – C alone.

5.1.3 : Third tier elimination

The CAMP website AMP prediction tool was used.All 4 outputs were consideredand only the peptides which scored a maximum positive result in at least 3 outputformats were selected. 14 peptides were selected out of this process. Among these,11 had a predicted net positive charge while 3 had predicted no net charge.

475.1.4 : Is it a signal peptide?Whether the designed peptides could be signal peptides or could contain a signalpeptide or not was predicted using the SignalP 4.1 Server. It was observed thatonly one peptide N – IWIWIWIWIW – C was predicted to be a signal peptide,while for all the other peptides, the prediction turned out to be negative. Even forthis peptide the prediction was positive only when the host was selected to beeukaryotic.

Figure 12a

48 Figure 12b Figure 12: SignalP 4.1 Results:

They show the C,S and Y scores as obtained for the peptide N – IWIWIWIWIW – C from theSignalP 4.1 server. The output was Name=IWIWIWIWIW_01 SP='YES' Cleavage site betweenpos. 2 and 3: W-IW D=0.460 D-cutoff=0.450 Networks=SignalP-noTM

5.1.5 : Anticancer properties

To test whether the 14 decapeptides had any anticancer properties, the AntiCPwebsite was used.(Tyagi et al., 2013) The SVM Score which shows a directcorrelation to the peptide’s anticancer properties(Tyagi et al., 2013), showed amaximum score of 0.99 only for N – KLKLKLKLKL – C and N –LKLKLKLKLK – C.The final 14 predicted antimicrobial peptides along with their different propertieshave been given in tables 2 and 3.5.1.6 : Secondary structure predictionThe secondary structures of the 14 peptides were determined using PEP-FOLD 3and the output was fed into Vega ZZ Molecular Modelling software. The clustercalculation was performed on all the peptides using LIQUID POPC as solvent,

49thickness = 20Å, overlap = 0.8Å, type = layer and cluster position as geometrycenter. The results are given from Figures 13 to 26.

The peptide’s secondary structure as predicted using PEP-FOLD (left) and N-RCRCRCRCR -Cpeptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right).

Figure 14 : N-KCKCKCKCKC-C

The peptide’s secondary structure as predicted using PEP-FOLD (left) and N-KCKCKCKCKC-C peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right)

53 Figure 15 : N-WRWRWRWRWR-C

The peptide’s secondary structure as predicted using PEP-FOLD (left) and N-

WRWRWRWRWR-C peptide visualized in a liquid POPC cluster. The blue molecules are watermolecules and the white molecules are POPC molecules.(right)

Figure 16 : N-RWRWRWRWRW-C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N-RWRWRWRWRW-

C peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right).

54 Figure 17 : N-KWKWKWKWKW–C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N-KWKWKWKWKW-

C peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right)

Figure 18 : N-WKWKWKWKWK-C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N-WKWKWKWKWK-

C peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right)

55 Figure 19 : N-IKIKIKIKIK-C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N-IKIKIKIKIK-C

peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right)

Figure 20 : N-KFKFKFKFKF–C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N-KFKFKFKFKF-C

peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right)

56 Figure 21 : N-FKFKFKFKFK–C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N-FKFKFKFKFK-C

peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right)

Figure 22 : N-KLKLKLKLKL–C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N-KLKLKLKLKL-C

peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right).

57 Figure 23 : N-LKLKLKLKLK–C

The peptide’s secondary as predicted by PEP-FOLD (left) and N-LKLKLKLKLK-C peptide

visualized in a liquid POPC cluster. The blue molecules are water molecules and the whitemolecules are POPC molecules.(right)

Figure 24 : N-CCCCCCCCCC–C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N-CCCCCCCCCC-C

peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right)

58 Figure 25 : N-WWWWWWWWWW-C

The peptide’s secondary structure as predicted by PEP-FOLD (left) and N -

peptide visualized in a liquid POPC cluster. The blue molecules are water molecules and thewhite molecules are POPC molecules.(right)

59As it can be seen from the above figures, all of the top 14 peptides becomecompletely surrounded by POPC molecules during clustering. This is a favorablecharacteristic for an antimicrobial peptide as it is observed that the most effectiveantimicrobial peptides are those that can get inserted into the phospholipid lipidbilayer (Wenzel et al., 2014).

5.1.7 : Selection of antimicrobial peptide for laboratory work

Out of the 14 peptides, the one having arginine and tryptophan was selected forproceeding to lab work based on previous evidence of its antimicrobialactivity(Wenzel et at., 2014).

5.1.8 : Plasmid design

The backbone was 2070 bp, the lac cassette is 1633 bp and the AMP sequence ( N- MWRWRWR - C ) is 21bp. The total size of the cassette was 3724bp.

61 Figure 27 : Plasmid design for the lac - AMP cassette.

5.2 : LABORATORY WORK

5.2.1 : Plasmid construction

The plasmid was constructed using the standard molecular biology techniques suchas restriction, ligation and polymerase chain reaction. The backbone pSB1C3 withthe lac promoter was cut using SpeI and PstI. The AMP sequence was separatelycut using XbaI and PstI. The ligation was done using T4 DNA ligase. Anextremely faint band was observed at around 4kb. This corresponds to the actual

62size of the antimicrobial peptide of 3724 kb.

Figure 28: The antimicrobial peptide cassette viewed in a gel.

5.3 : DISCUSSION

This project was done to see whether really small peptides can act as antimicrobialpeptides. There appear to be some characteristics that appear to be essential for apeptide to display antimicrobial properties. These include the peptide length,amino acid composition, hydrophobicity of the amino acids(Wenzel et al., 2014),the peptide secondary structure, the overall charge of the peptide and whether thepeptide is a signal peptide. Antimicrobial peptides usually have a large proportionof hydrophobic residues. Positively charged amino acids like arginine and lysineare also favored to be a part of antimicrobial peptides.(Wenzel et al., 2014) The

63antimicrobial activity also increases as the peptide loses all its non antimicrobialregions i.e. as the peptide becomes smaller. It is seen that AMPs are usually one ofthe 4 classes i) α helical, ii) β stranded due to the presence of 2 or more disulfidebonds, iii) β-hairpin or loop due to the presence of a single disulfide bond and/orcyclization of the peptide chain, and iv) extended. Sometimes they can also havean undefined structure.

It is observed that among the 14 best peptides predicted to have a good

antimicrobial activity in this study, the only ones with the α helical conformationare N-RWRWRWRWRW-C, N-WRWRWRWRWR-C, N-WWWWWWWWW-C, N-IWIWIWIWIW-C. Though the peptide N-CCCCCCCCCC-C does not haveany hydrophobic residues, it still shows good promise, since it passed all threelevels of AMP prediction.

The cluster calculation shows that all the 14 peptides show insertion into theLiquid POPC solvent. POPC is used to as a substitute for phospholipid bilayer. Theresults observed are consistent with the fact that many good antimicrobial peptidesshow insertion into the phospholipid bilayer(Wenzel et al., 2014).

For testing the AMP in the lab, the sequence N-MWRWRWR-C was selectedbecause 1. It showed promise as an antimicrobial peptide during the study and 2.There was experimental evidence that a hexapeptide having alternating arginineand tryptophan has good antimicrobial activity.

The ligated AMP cassette was successfully created in the laboratory, and the resultis shown in Figure 28.

64 CHAPTER 6 CONCLUSION

175 decapeptides were proposed and 14 peptides were finalized as good

antimicrobial candidates based on antimicrobial index, instability index,hydrophobicity and other properties. The finalized peptides were modelled andcluster calculation was performed on all the peptides using Liquid POPC assolvent. A plasmid construct was designed using only arginine and tryptophan forthe antimicrobial peptide and the plasmid was constructed in the laboratory.Arginine and tryptophan were used because1. They have relatively low antimicrobial index (Torrent et al., 2011)2. The present study proved that arginine and tryptophan can be a part of aneffective antimicrobial peptide.3. A hexapeptide having only areginine and tryptophan was proved to have anexcellent antimicrobial effect.(Wenzel et al., 2014)It was then transformed into E.coli and the band visualized in an agarose gel usingelectrophoresis. In the future, the 14 peptides have to aligned with well knownAMPs and their physical and chemical properties should also be compared.